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An Axiomatic Approach for Result Diversification (2009)
| Venue: | WWW 2009 MADRID! |
| Citations: | 104 - 1 self |
BibTeX
@MISC{Gollapudi09anaxiomatic,
author = {Sreenivas Gollapudi and Aneesh Sharma},
title = {An Axiomatic Approach for Result Diversification},
year = {2009}
}
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Abstract
Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal, and hence the search engine would like to trade-off relevance for diversity in the results. In analogy to prior work on ranking and clustering systems, we use the axiomatic approach to characterize and design diversification systems. We develop a set of natural axioms that a diversification system is expected to satisfy, and show that no diversification function can satisfy all the axioms simultaneously. We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms. We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives. Finally, we propose an evaluation methodology to characterize the objectives and the underlying axioms. We conduct a large scale evaluation of our objectives based on two data sets: a data set derived from the Wikipedia disambiguation pages and a product database.
Keyphrases
axiomatic approach result diversification user intent data set search engine diversification system relevant result different subset wikipedia disambiguation page underlying axiom axiomatic framework user satisfaction diversification objective product database diversification function example diversification trade-off relevance principle-based approach explicit knowledge evaluation methodology rich link effective ranking system facility dispersion problem prior work natural axiom large scale evaluation
